Authors: Biswal B.B., Mennes M., Zuo X.-N., Gohel S., Kelly C., Smith S.M., Beckmann C.F., Adelstein J.S., Buckner R.L., Colcombe S., Dogonowski A.-M., Ernst M., Fair D., Hampson M., Hoptman M.J., Hyde J.S., Kiviniemi V.J., Kotter R., Li S.-J., Lin C.-P., Lowe M.J., Mackay C., Madden D.J., Madsen K.H., Margulies D.S., Mayberg H.S., McMahon K., Monk C.S., Mostofsky S.H., Nagel B.J., Pekar J.J., Peltier S.J., Petersen S.E., Riedl V., Rombouts S.A.R.B., Rypma B., Schlaggar B.L., Schmidt S., Seidler R.D., Siegle G.J., S
Publication Date: March, 2010
Although it is being successfully implemented for exploration of
the genome, discovery science has eluded the functional neuroimaging
community. The core challenge remains the development
of common paradigms for interrogating the myriad functional
systems in the brain without the constraints of a priori hypotheses.
Resting-state functional MRI (R-fMRI) constitutes a candidate
approach capable of addressing this challenge. Imaging the brain
during rest reveals large-amplitude spontaneous low-frequency
(<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated
across functionally related areas. Referred to as functional
connectivity, these correlations yield detailed maps of complex
neural systems, collectively constituting an individual’s “functional
connectome.” Reproducibility across datasets and individuals suggests
the functional connectome has a common architecture, yet
each individual’s functional connectome exhibits unique features,
with stable, meaningful interindividual differences in connectivity
patterns and strengths. Comprehensive mapping of the functional
connectome, and its subsequent exploitation to discern genetic
influences and brain–behavior relationships, will require multicenter
collaborative datasets. Here we initiate this endeavor by gathering
R-fMRI data from 1,414 volunteers collected independently
at 35 international centers. We demonstrate a universal architecture
of positive and negative functional connections, as well as
consistent loci of inter-individual variability. Age and sex emerged
as significant determinants. These results demonstrate that independent
R-fMRI datasets can be aggregated and shared. Highthroughput
R-fMRI can provide quantitative phenotypes for
molecular genetic studies and biomarkers of developmental and
pathological processes in the brain. To initiate discovery science of
brain function, the 1000 Functional Connectomes Project dataset is
freely accessible at www.nitrc.org/projects/fcon_1000/.
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